The Chemical Looping Process
A typical chemical looping (CL) system utilizes a high temperature process, whereby solids such as calcium- or metal-based compounds, for example, are “looped” between a first reactor, called an oxidizer, and a second reactor, called a reducer. In the oxidizer, oxygen from air injected into the oxidizer is captured by the solids in an oxidation reaction. The captured oxygen is then carried by the oxidized solids to the reducer to be used for combustion and/or gasification of a fuel such as coal, for example. After a reduction reaction in the reducer, the solids, no longer having the captured oxygen, are returned to the oxidizer to be oxidized again, and the cycle repeats.
Depending on a ratio of the fuel to the air, different gases are produced in the oxidation and reduction reactions. As a result, the ratio of fuel to air can be controlled such that the CL system may be utilized in different ways, such as: as a hybrid combustion-gasification process which produces hydrogen for gas turbines, fuel cells and/or other hydrogen-based applications; as a hybrid combustion-gasification process which produces a synthesis gas (syngas) containing varying amounts of hydrogen and carbon dioxide for gas turbines and/or fuel cells; or as a combustion process for a combustion-based steam power plant.
The CL process is more complicated than processes of traditional plants such as conventional circulating fluidized bed (CFB) plants, for example. As a result, traditional plant controls applied to the CL process necessarily result in separate control loops for each CL loop. However, using separate control loops for each CL loop is inefficient and does not optimize performance of the CL process, since accurate control depends on coordinated control of multiple parameters in each loop, and parameters which crossover between loops.
In addition, the CL process has multi-phase flows and chemical reactions which are characterized by process nonlinearities and time delays due to mass transport and chemical reaction rates. As a result, traditional power plant design without considering control optimization systems in early stages of process design are further inadequate for integrated optimization of process performance and system operability.
Further, many of the variables in the CL process have nonlinear relationships with other variables, e.g., inter-loop interaction of variables. As a result, process models need to be developed so as to effectively characterize these multi-interdependent variable relationships.
Chemical looping technology is a method of heat production that can produce a separate stream of CO2 that can be sequestered, reducing the exhaust of greenhouse gases. This concept is based on a process utilizing high temperature chemical and thermal looping technology. As studied in previous projects, the chemical looping plant was assessed very favorably in terms of capital cost and electricity cost with up to 95% CO2 capture. However, due to the inherent nonlinearity of the process and the multi-loop interactions of solid particles, it is a quite challenging problem to control the particle flows and stabilize the reactants (solids) transport in the loops such that the system can sustain desired chemical reactions and provide stable energy production.
Nonlinear Model Predictive Control
In order to achieve the goals of stability and maximum profitability for the chemical looping process, the design of advanced process control becomes one of the important components in the development of this technology. Model predictive control (“MPC”) is an advanced method of model based process control. It is a multivariable control algorithm that uses an internal dynamic model of the process and an optimization solver to calculate the optimum control moves. MPC schemes that are based on nonlinear models and consider linear or non-linear cost-functions and general nonlinear constraints on the state and input variables are considered nonlinear model predictive control (NMPC). Nonlinear model predictive control (NMPC) is presented schematically in FIG. 1.
Values are provided for input variables (or manipulated variables) to a plant 1, that is intended to be controlled. The plant 1 produces outputs that are fed to a NMPC 100 that includes an internal nonlinear model 120 that is defined by non-linear equations between at least one input and at least one output.
Nonlinear model 120 is a mathematical model of various processes of plant 1 that provide outputs similar to plant 1 when each are supplied with the same inputs.
NMPC 100 also includes a nonlinear optimizer 130. The nonlinear optimizer 130 receives input constraint ranges and at least one goal. Nonlinear optimizer 130 provides input values within the constraint range to the nonlinear model 120 which creates outputs. Nonlinear optimizer 130 monitors and stores the outputs of the nonlinear model 120. Nonlinear optimizer 130 repeats this process for a plurality of input variable values spanning the constraint range while monitoring and storing the outputs. It then analyzes the outputs and goals to determine an optimum output and the inputs associated with the optimum output.
An estimator 110 interacts with nonlinear model 120 to estimate the values of internal state variables for given input and output variables.
To develop an NMPC a mathematical model of the chemical looping system must be designed which accurately depicts the functioning of the chemical looping system and its control structures.
Usually these models solve non-linear problems, and are therefore are computationally demanding due to the large number of computations required for each output calculation. Therefore, to be practical, there must be a way to use the model to arrive at estimated output quickly.
It is important to consider costs of running a chemical looping plant. Therefore, one of the control goals should include optimization of operating costs instead of simply optimizing operation. Therefore, there is currently a need for a controller for a chemical looping process that can stabilize its operation and minimize its operating costs.
The above described and other features are exemplified by the following figures and detailed description.